Internode Data Communications In A Parallel Computer

ABSTRACT

Internode data communications in a parallel computer that includes compute nodes that each include main memory and a messaging unit, the messaging unit including computer memory and coupling compute nodes for data communications, in which, for each compute node at compute node boot time: a messaging unit allocates, in the messaging unit&#39;s computer memory, a predefined number of message buffers, each message buffer associated with a process to be initialized on the compute node; receives, prior to initialization of a particular process on the compute node, a data communications message intended for the particular process; and stores the data communications message in the message buffer associated with the particular process. Upon initialization of the particular process, the process establishes a messaging buffer in main memory of the compute node and copies the data communications message from the message buffer of the messaging unit into the message buffer of main memory.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

This invention was made with Government support under Contract No.B554331 awarded by the Department of Energy. The Government has certainrights in this invention.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The field of the invention is data processing, or, more specifically,methods, apparatus, and products for internode data communications in aparallel computer.

2. Description of Related Art

The development of the EDVAC computer system of 1948 is often cited asthe beginning of the computer era. Since that time, computer systemshave evolved into extremely complicated devices. Today's computers aremuch more sophisticated than early systems such as the EDVAC. Computersystems typically include a combination of hardware and softwarecomponents, application programs, operating systems, processors, buses,memory, input/output devices, and so on. As advances in semiconductorprocessing and computer architecture push the performance of thecomputer higher and higher, more sophisticated computer software hasevolved to take advantage of the higher performance of the hardware,resulting in computer systems today that are much more powerful thanjust a few years ago.

Parallel computing is an area of computer technology that hasexperienced advances. Parallel computing is the simultaneous executionof the same application (split up and specially adapted) on multipleprocessors in order to obtain results faster. Parallel computing isbased on the fact that the process of solving a problem usually can bedivided into smaller jobs, which may be carried out simultaneously withsome coordination.

Parallel computers execute parallel algorithms. A parallel algorithm canbe split up to be executed a piece at a time on many differentprocessing devices, and then put back together again at the end to get adata processing result. Some algorithms are easy to divide up intopieces. Splitting up the job of checking all of the numbers from one toa hundred thousand to see which are primes could be done, for example,by assigning a subset of the numbers to each available processor, andthen putting the list of positive results back together. In thisspecification, the multiple processing devices that execute theindividual pieces of a parallel program are referred to as ‘computenodes.’ A parallel computer is composed of compute nodes and otherprocessing nodes as well, including, for example, input/output (‘I/O’)nodes, and service nodes.

Parallel algorithms are valuable because it is faster to perform somekinds of large computing jobs via a parallel algorithm than it is via aserial (non-parallel) algorithm, because of the way modern processorswork. It is far more difficult to construct a computer with a singlefast processor than one with many slow processors with the samethroughput. There are also certain theoretical limits to the potentialspeed of serial processors. On the other hand, every parallel algorithmhas a serial part and so parallel algorithms have a saturation point.After that point adding more processors does not yield any morethroughput but only increases the overhead and cost.

Parallel algorithms are designed also to optimize one more resource thedata communications requirements among the nodes of a parallel computer.There are two ways parallel processors communicate, shared memory ormessage passing. Shared memory processing needs additional locking forthe data and imposes the overhead of additional processor and bus cyclesand also serializes some portion of the algorithm.

Message passing processing uses high-speed data communications networksand message buffers, but this communication adds transfer overhead onthe data communications networks as well as additional memory need formessage buffers and latency in the data communications among nodes.Designs of parallel computers use specially designed data communicationslinks so that the communication overhead will be small but it is theparallel algorithm that decides the volume of the traffic.

Many data communications network architectures are used for messagepassing among nodes in parallel computers. Compute nodes may beorganized in a network as a ‘torus’ or ‘mesh,’ for example. Also,compute nodes may be organized in a network as a tree. A torus networkconnects the nodes in a three-dimensional mesh with wrap around links.Every node is connected to its six neighbors through this torus network,and each node is addressed by its x,y,z coordinate in the mesh. In atree network, the nodes typically are connected into a binary tree: eachnode has a parent and two children (although some nodes may only havezero children or one child, depending on the hardware configuration). Incomputers that use a torus and a tree network, the two networkstypically are implemented independently of one another, with separaterouting circuits, separate physical links, and separate message buffers.

A torus network lends itself to point to point operations, but a treenetwork typically is inefficient in point to point communication. A treenetwork, however, does provide high bandwidth and low latency forcertain collective operations, message passing operations where allcompute nodes participate simultaneously, such as, for example, anallgather.

SUMMARY OF THE INVENTION

Conventionally in distributed processing systems running parallelapplications, all parallel processes must be initialized together. Thisrequires that all processes are available from the very beginning—atstartup of the parallel application. As such, processes may not beinitialized later to expand processing resources on demand. Further, inmany parallel computers, a process must establish a data communicationsreception buffer upon initialization. Here, two problems are created.First, no other process may send data to an uninitialized processbecause no reception buffer exists. Second, an asynchronouslyinitialized sending process has to check that a receiving process isinitialized before sending data to the process. Such a check isexpensive, in terms of time and execution cycles, and does not scalewell.

To that end, this specification sets forth methods, apparatus, andproduct for internode data communications in a parallel computer aredisclosed. The parallel computer includes a number of compute nodes,with each compute node including main computer memory and a messagingunit. The messaging unit includes computer memory as well and isimplemented as a module of automated computing machinery that couplescompute nodes for data communications. Internode data communications iscarried out in such a parallel computer in accordance with embodimentsof the present invention by, for each compute node at compute node boottime: allocating, by the messaging unit in the messaging unit's computermemory, a predefined number of message buffers, each message bufferassociated with a process to be initialized on the compute node;receiving, by the messaging unit prior to initialization of a particularprocess on the compute node, a data communications message intended forthe particular process; storing the data communications message in themessage buffer associated with the particular process; uponinitialization of the particular process, establishing, by theparticular process, a messaging buffer in main memory of the computenode; and copying, by the particular process, the data communicationsmessage from the message buffer of the messaging unit into the messagebuffer of main memory.

This specification also sets forth methods, apparatus, and products forspeculative internode data communications in a parallel computer thatincludes a plurality of compute nodes with each compute node configuredto execute a plurality of processes. Speculative internode datacommunications in accordance with embodiments of the present inventionincludes: allocating, at boot time of a compute node by the messagingunit of the compute node, a message buffer in the messaging unit'scomputer memory, the message buffer associated with a potential processregardless of whether the potential process has been initialized;receiving, by the messaging unit, a data communications message intendedfor the potential process regardless of whether the potential processhas been initialized; and storing the data communications message in themessage buffer for the potential process regardless of whether thepotential process has been initialized.

The foregoing and other objects, features and advantages of theinvention will be apparent from the following more particulardescriptions of exemplary embodiments of the invention as illustrated inthe accompanying drawings wherein like reference numbers generallyrepresent like parts of exemplary embodiments of the invention.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 sets forth a block and network diagram of an example parallelcomputer that performs internode data communications according toembodiments of the present invention.

FIG. 2 sets forth a block diagram of an example compute node for use ina parallel computer that performs internode data communicationsaccording to embodiments of the present invention.

FIG. 3A illustrates an example of a Point To Point Adapter useful inparallel computers that perform internode data communications accordingto embodiments of the present invention.

FIG. 3B illustrates an example of a Collective Operations Adapter usefulin a parallel computer that performs internode data communications in aparallel computer according to embodiments of the present invention.

FIG. 4 sets forth a line drawing illustrating an example datacommunications network optimized for point-to-point operations useful inparallel computers that perform internode data communications in aparallel computer according to embodiments of the present invention.

FIG. 5 illustrates an example data communications network optimized forcollective operations by organizing compute nodes in a tree.

FIG. 6 sets forth a block diagram of an example protocol stack useful inparallel computers that perform internode data communications accordingto embodiments of the present invention.

FIG. 7 sets forth a functional block diagram of an example PAMI for usein parallel computers that perform internode data communicationsaccording to embodiments of the present invention.

FIG. 8A sets forth a block diagram of example data communicationsresources useful in parallel computers that perform internode datacommunications according to embodiments of the present invention.

FIG. 8B sets forth a functional block diagram of an example DMAcontroller operatively coupled to a network—in an architecture wherethis DMA controller is the only DMA controller on a compute node—and anorigin endpoint and its target endpoint are both located on the samecompute node.

FIG. 9 sets forth a functional block diagram of an example PAMI usefulin parallel computers that perform internode data communicationsaccording to embodiments of the present invention in which the examplePAMI operates, on behalf of an application, with multiple applicationmessaging modules simultaneously.

FIG. 10 sets forth a functional block diagram of example endpointsuseful in parallel computers that process perform internode datacommunications in a parallel computer according to embodiments of thepresent invention.

FIG. 11 sets forth a flow chart illustrating an example method ofinternode data communications in a parallel computer according toembodiments of the present invention.

FIG. 12 sets forth a flow chart illustrating a further example method ofinternode data communications in a parallel computer according toembodiments of the present invention.

FIG. 13 sets forth a flow chart illustrating a further example method ofinternode data communications in a parallel computer according toembodiments of the present invention.

DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS

Example methods, computers, and computer program products for internodedata communications in a parallel computer according to embodiments ofthe present invention are described with reference to the accompanyingdrawings, beginning with FIG. 1. FIG. 1 sets forth a block and networkdiagram of an example parallel computer (100) that carries out internodedata communications according to embodiments of the present invention.The parallel computer (100) in the example of FIG. 1 is coupled tonon-volatile memory for the computer in the form of data storage device(118), an output device for the computer in the form of printer (120),and an input/output device for the computer in the form of computerterminal (122).

The parallel computer (100) in the example of FIG. 1 includes aplurality of compute nodes (102). The compute nodes (102) are coupledfor data communications by several independent data communicationsnetworks including a high speed Ethernet network (174), a Joint TestAction Group (‘JTAG’) network (104), a tree network (106) which isoptimized for collective operations, and a torus network (108) which isoptimized point to point operations. Tree network (106) is a datacommunications network that includes data communications links connectedto the compute nodes so as to organize the compute nodes as a tree. Eachdata communications network is implemented with data communicationslinks among the compute nodes (102). The data communications linksprovide data communications for parallel operations among the computenodes of the parallel computer.

In addition, the compute nodes (102) of parallel computer (100) areorganized into at least one operational group (132) of compute nodes forcollective parallel operations on parallel computer (100). Anoperational group of compute nodes is the set of compute nodes uponwhich a collective parallel operation executes. Collective operationsare implemented with data communications among the compute nodes of anoperational group. Collective operations are those functions thatinvolve all the compute nodes of an operational group. A collectiveoperation is an operation, a message-passing computer programinstruction that is executed simultaneously, that is, at approximatelythe same time, by all the compute nodes in an operational group ofcompute nodes. Such an operational group may include all the computenodes in a parallel computer (100) or a subset all the compute nodes.Collective operations are often built around point to point operations.A collective operation requires that all processes on all compute nodeswithin an operational group call the same collective operation withmatching arguments. A ‘broadcast’ is an example of a collectiveoperations for moving data among compute nodes of an operational group.A ‘reduce’ operation is an example of a collective operation thatexecutes arithmetic or logical functions on data distributed among thecompute nodes of an operational group. An operational group may beimplemented as, for example, an MPI ‘communicator.’

‘MPI’ refers to ‘Message Passing Interface,’ a prior art applicationsmessaging module or parallel communications library, anapplication-level messaging module of computer program instructions fordata communications on parallel computers. Such an application messagingmodule is disposed in an application messaging layer in a datacommunications protocol stack. Examples of prior-art parallelcommunications libraries that may be improved for use with parallelcomputers that perform internode data communications according toembodiments of the present invention include IBM's MPI library, the‘Parallel Virtual Machine’ (‘PVM’) library, MPICH, OpenMPI, and LAM/MPI.MPI is promulgated by the MPI Forum, an open group with representativesfrom many organizations that define and maintain the MPI standard. MPIat the time of this writing is a de facto standard for communicationamong compute nodes running a parallel program on a distributed memoryparallel computer. This specification sometimes uses MPI terminology forease of explanation, although the use of MPI as such is not arequirement or limitation of the present invention.

Most collective operations are variations or combinations of four basicoperations: broadcast, gather, scatter, and reduce. In a broadcastoperation, all processes specify the same root process, whose buffercontents will be sent. Processes other than the root specify receivebuffers. After the operation, all buffers contain the message from theroot process.

A scatter operation, like the broadcast operation, is also a one-to-manycollective operation. All processes specify the same receive count. Thesend arguments are only significant to the root process, whose bufferactually contains sendcount*N elements of a given datatype, where N isthe number of processes in the given group of compute nodes. The sendbuffer will be divided equally and dispersed from the root to allprocesses (including the root). Each process is assigned a sequentialidentifier termed a ‘rank.’ After the operation, the root has sentsendcount data elements to each process in increasing rank order. Rank 0(the root process) receives the first sendcount data elements from thesend buffer. Rank 1 receives the second sendcount data elements from thesend buffer, and so on.

A gather operation is a many-to-one collective operation that is acomplete reverse of the description of the scatter operation. That is, agather is a many-to-one collective operation in which elements of adatatype are gathered from the ranked processes into a receive buffer ofthe root process.

A reduce operation is also a many-to-one collective operation thatincludes an arithmetic or logical function performed on two dataelements. All processes specify the same ‘count’ and the same arithmeticor logical function. After the reduction, all processes have sent countdata elements from compute node send buffers to the root process. In areduction operation, data elements from corresponding send bufferlocations are combined pair-wise by arithmetic or logical operations toyield a single corresponding element in the root process's receivebuffer. Application specific reduction operations can be defined atruntime. Parallel communications libraries may support predefinedoperations. MPI, for example, provides the following pre-definedreduction operations:

-   -   MPI_MAX maximum    -   MPI_MIN minimum    -   MPI_SUM sum    -   MPI_PROD product    -   MPI_LAND logical AND    -   MPI_BAND bitwise AND    -   MPI_LOR logical OR    -   MPI_BOR bitwise OR    -   MPI_LXOR logical exclusive OR    -   MPI_BXOR bitwise exclusive OR

In addition to compute nodes, the example parallel computer (100)includes input/output (‘I/O’) nodes (110, 114) coupled to compute nodes(102) through one of the data communications networks (174). The I/Onodes (110, 114) provide I/O services between compute nodes (102) andI/O devices (118, 120, 122). I/O nodes (110, 114) are connected for datacommunications I/O devices (118, 120, 122) through local area network(‘LAN’) (130). Computer (100) also includes a service node (116) coupledto the compute nodes through one of the networks (104). Service node(116) provides service common to pluralities of compute nodes, loadingprograms into the compute nodes, starting program execution on thecompute nodes, retrieving results of program operations on the computenodes, and so on. Service node (116) runs a service application (124)and communicates with users (128) through a service applicationinterface (126) that runs on computer terminal (122).

As the term is used here, a parallel active messaging interface or‘PAMI’ (218) is a system-level messaging layer in a protocol stack of aparallel computer that is composed of data communications endpoints eachof which is specified with data communications parameters for a threadof execution on a compute node of the parallel computer. The PAMI is a‘parallel’ interface in that many instances of the PAMI operate inparallel on the compute nodes of a parallel computer. The PAMI is an‘active messaging interface’ in that data communications messages in thePAMI are active messages, ‘active’ in the sense that such messagesimplement callback functions to advise of message dispatch andinstruction completion and so on, thereby reducing the quantity ofacknowledgment traffic, and the like, burdening the data communicationresources of the PAMI.

Each data communications endpoint of a PAMI is implemented as acombination of a client, a context, and a task. A ‘client’ as the termis used in PAMI operations is a collection of data communicationsresources dedicated to the exclusive use of an application-level dataprocessing entity, an application or an application messaging modulesuch as an MPI library. A ‘context’ as the term is used in PAMIoperations is composed of a subset of a client's collection of dataprocessing resources, context functions, and a work queue of datatransfer instructions to be performed by use of the subset through thecontext functions operated by an assigned thread of execution. In atleast some embodiments, the context's subset of a client's dataprocessing resources is dedicated to the exclusive use of the context. A‘task’ as the term is used in PAMI operations refers to a canonicalentity, an integer or objection oriented programming object, thatrepresents in a PAMI a process of execution of the parallel application.That is, a task is typically implemented as an identifier of aparticular instance of an application executing on a compute node, acompute core on a compute node, or a thread of execution on amulti-threading compute core on a compute node. In the example of FIG.1, the compute nodes (102), as well as PAMI endpoints on the computenodes, are coupled for data communications through the PAMI (218) andthrough data communications resources such as collective network (106)and point-to-point network (108).

Conventionally in distributed processing systems running parallelapplications, all parallel processes must be initialized together. Thisrequires that all processes are available from the very beginning—atstartup of the parallel application. As such, processes may not beinitialized later to expand processing resources on demand. Further, inmany parallel computers, a process must establish a data communicationsreception buffer upon initialization. Here, two problems are created.First, no other process may send data to an uninitialized processbecause no reception buffer exists. Second, an asynchronouslyinitialized sending process has to check that a receiving process isinitialized before sending data to the process. Such a check isexpensive, in terms of time and execution cycles, and does not scalewell.

The example parallel computer (100) of FIG. 1 is improved to carry outinternode data communications according to embodiments of the presentinvention. Each compute node (102) in the example of FIG. 1 isconfigured to execute a plurality of processes. Such a process may be aprocess in PAMI (a PAMI endpoint, for example), a process representingan instance of an application, or other type of process. Each computenode also includes main computer memory (156) and a messaging unit. Themessaging unit, sometimes implemented as a DMA controller, includescomputer memory (138) and is a module of automated computing machinerythat couples compute nodes for data communications.

At boot time of each compute node (102) in the example parallel computer(100) of FIG. 1, the messaging unit allocates, in the messaging unit'scomputer memory (138), a predefined number of message buffers (140).Each message buffer is associated with a process to be initialized onthe compute node. Each message buffer may include a reception FIFO(first-in, first-out) buffer and an injection FIFO buffer for a process.The message buffers (140) are established without regard to whether theprocess associated which the message buffer has actually beeninitialized.

The messaging unit is configured to receive, prior to initialization ofa particular process (134) on the compute node (102), a datacommunications message (146) intended for the particular process. Thedata communications message is received from a process executing on adifferent compute node. The messaging unit stores the datacommunications message (146) in the message buffer (140) associated withthe particular process (134) and, upon initialization of the particularprocess, the particular process establishes a messaging buffer (136) inmain memory (156) of the compute node and copies the data communicationsmessage (146) from the message buffer (140) of the messaging unit intothe message buffer (136) of main memory (156). In this way, processesmay send data to another process on another compute node regardless ofwhether the process on the other compute node has been initialized.

The example parallel computer (100) of FIG. 1 may also configured forspeculative internode data communications in accordance with embodimentsof the present invention. In the example of FIG. 1, the messaging unitallocates, at boot time of a compute node (102), a message buffer (140)in the MU memory (138), the message buffer (140) associated with apotential process regardless of whether the potential process (134) hasbeen initialized. The messaging unit receives a data communicationsmessage (146) intended for the potential process (134) regardless ofwhether the potential process (134) has been initialized and stores thedata communications message (146) in the message buffer (140) for thepotential process (134) regardless of whether the potential process hasbeen initialized. The phrase ‘potential’ is used here to describe aprocess that may, or may not, be initialized at the present time or inthe future. For example, a first process may send a data communicationsmessage to a ‘potential’ process and, at the time of sending, thepotential process may be in any one of several execution states: notinitialized; in the process of initializing; and initialized andexecuting. Assuming the potential process has not been fully initializedat the time of sending the data communications message, the potentialprocess may never completely initialize. In this way, the process issaid to be a ‘potential’ process.

Given the ‘potential’ nature of processes, the system of FIG. 1 may beconfigured to ensure delivery of the data communications messages. Toensure completed delivery of data communications in such a system, thefirst process may be configured to periodically poll, for a predefinedamount of time, the second process's message buffer to determine whetherthe data communications message has been retrieved by the secondprocess. If the data communications message has not been retrievedduring the predefined amount of time, the first process may flush thesecond process's message buffer (overwriting the data communicationsmessage) and send the data communications message to another process.Readers of skill in the art will recognize, that polling the secondprocess's message buffer for a predefined amount of time is but one way,among many possible ways, to ensure delivery of a data communicationsmessage to an uninitialized process. In another example, processes maybe configured to send acknowledgments of receipt of data communicationsmessage. In such an example, a first process may be configured to sendthe data communications message to a second process, wait foracknowledgement from the second process for a predefined amount of time,and send the data communications message to another process if the firstprocess does not receive an acknowledgement from the second processafter the predefined amount of time.

The arrangement of compute nodes, networks, and I/O devices making upthe example parallel computer illustrated in FIG. 1 are for explanationonly, not for limitation of the present invention. Parallel computerscapable of internode data communications according to embodiments of thepresent invention may include additional nodes, networks, devices, andarchitectures, not shown in FIG. 1, as will occur to those of skill inthe art. For ease of explanation, the parallel computer in the exampleof FIG. 1 is illustrated with only four processors (614, 624) in acompute node. Readers will recognize that compute nodes in parallelcomputers that perform internode data communications according toembodiments of the present invention can include any number ofprocessors as may occur to those of skill in the art; each compute nodein IBM's BlueGene/Q supercomputer, for example, includes 16 applicationprocessors and a management processor. The parallel computer (100) inthe example of FIG. 1 includes sixteen compute nodes (102); parallelcomputers that perform internode data communications according to someembodiments of the present invention include thousands of compute nodes.In addition to Ethernet and JTAG, networks in such data processingsystems may support many data communications protocols including forexample TCP (Transmission Control Protocol), IP (Internet Protocol), andothers as will occur to those of skill in the art. Various embodimentsof the present invention may be implemented on a variety of hardwareplatforms in addition to those illustrated in FIG. 1.

Internode data communications in a parallel computer according toembodiments of the present invention is generally implemented on aparallel computer that includes a plurality of compute nodes. In fact,such computers may include thousands of such compute nodes, with acompute node typically executing at least one instance of a parallelapplication. Each compute node is in turn itself a computer composed ofone or more computer processors, its own computer memory, and its owninput/output (‘I/O’) adapters. For further explanation, therefore, FIG.2 sets forth a block diagram of an example compute node (152) for use ina parallel computer that perform internode data communications accordingto embodiments of the present invention. The compute node (152) of FIG.2 includes one or more computer processors (164) as well as randomaccess memory (RAM') (156). Each processor (164) can support multiplehardware compute cores (165), and each such core can in turn supportmultiple threads of execution, hardware threads of execution as well assoftware threads. Each processor (164) is connected to RAM (156) througha high-speed front side bus (161), bus adapter (194), and a high-speedmemory bus (154)—and through bus adapter (194) and an extension bus(168) to other components of the compute node. Stored in RAM (156) is anapplication program (158), a module of computer program instructionsthat carries out parallel, user-level data processing using parallelalgorithms.

Also stored RAM (156) is an application messaging module (216), alibrary of computer program instructions that carry outapplication-level parallel communications among compute nodes, includingpoint to point operations as well as collective operations. Although theapplication program can call PAMI routines directly, the applicationprogram (158) often executes point-to-point data communicationsoperations by calling software routines in the application messagingmodule (216), which in turn is improved according to embodiments of thepresent invention to use PAMI functions to implement suchcommunications. An application messaging module can be developed fromscratch to use a PAMI according to embodiments of the present invention,using a traditional programming language such as the C programminglanguage or C++, for example, and using traditional programming methodsto write parallel communications routines that send and receive dataamong PAMI endpoints and compute nodes through data communicationsnetworks or shared-memory transfers. In this approach, the applicationmessaging module (216) exposes a traditional interface, such as MPI, tothe application program (158) so that the application program can gainthe benefits of a PAMI with no need to recode the application. As analternative to coding from scratch, therefore, existing prior artapplication messaging modules may be improved to use the PAMI, existingmodules that already implement a traditional interface. Examples ofprior-art application messaging modules that can be improved toimplement internode data communications in a parallel computer accordingto embodiments of the present invention include such parallelcommunications libraries as the traditional ‘Message Passing Interface’(‘MPI’) library, the ‘Parallel Virtual Machine’ (‘PVM’) library, MPICH,and the like.

Also represented in RAM in the example of FIG. 2 is a PAMI (218).Readers will recognize, however, that the representation of the PAMI inRAM is a convention for ease of explanation rather than a limitation ofthe present invention, because the PAMI and its components, endpoints,clients, contexts, and so on, have particular associations with andinclusions of hardware data communications resources. In fact, the PAMIcan be implemented partly as software or firmware and hardware—or even,at least in some embodiments, entirely in hardware.

Also represented in RAM (156) in the example of FIG. 2 is a segment(227) of shared memory. In typical operation, the operating system (162)in this example compute node assigns portions of address space to eachprocessor (164), and, to the extent that the processors include multiplecompute cores (165), treats each compute core as a separate processorwith its own assignment of a portion of core memory or RAM (156) for aseparate heap, stack, memory variable storage, and so on. The defaultarchitecture for such apportionment of memory space is that eachprocessor or compute core operates its assigned portion of memoryseparately, with no ability to access memory assigned to anotherprocessor or compute core. Upon request, however, the operating systemgrants to one processor or compute core the ability to access a segmentof memory that is assigned to another processor or compute core, andsuch a segment is referred to in this specification as a ‘segment ofshared memory.’

In the example of FIG. 2, each processor or compute core has uniformaccess to the RAM (156) on the compute node, so that accessing a segmentof shared memory is equally fast regardless where the shared segment islocated in physical memory. In some embodiments, however, modules ofphysical memory are dedicated to particular processors, so that aprocessor may access local memory quickly and remote memory more slowly,a configuration referred to as a Non-Uniform Memory Access or ‘NUMA.’ Insuch embodiments, a segment of shared memory can be configured locallyfor one endpoint and remotely for another endpoint—or remotely from bothendpoints of a communication. From the perspective of an origin endpointtransmitting data through a segment of shared memory that is configuredremotely with respect to the origin endpoint, transmitting data throughthe segment of shared memory will appear slower that if the segment ofshared memory were configured locally with respect to the originendpoint—or if the segment were local to both the origin endpoint andthe target endpoint. This is the effect of the architecture representedby the compute node (152) in the example of FIG. 2 with all processorsand all compute cores coupled through the same bus to the RAM—that allaccesses to segments of memory shared among processes or processors onthe compute node are local—and therefore very fast.

The example compute node (152) of FIG. 2 is also configured with amessaging unit—implemented in this example as a DMA controller (225).The DMA controller (225) is coupled to messaging unit memory (138). Insome embodiments, the MU memory (138) is of a predefined size, muchsmaller than main memory. That is, memory resources available to the DMAcontroller may be sparse in some embodiments. The DMA controller (225)supports internode data communications in a parallel computer inaccordance with embodiments of the present invention by: allocating, atboot time of the compute node (152) in the MU memory (138), a predefinednumber of message buffers (140), where each message buffer is associatedwith a process to be initialized on the compute node. The DMA controller(225) may receive, prior to initialization of a particular process (134)on the compute node (152), a data communications message (146) intendedfor the particular process (134) and may store the data communicationsmessage in the message buffer (140) associated with the particularprocess. Upon initialization of the particular process (134), theparticular process (134) may establish a messaging buffer (136) in mainmemory (156) of the compute node (152) and may copy the datacommunications message (146) from the MU memory (138) message buffer(140) into the message buffer (136) of main memory (156).

Also stored in RAM (156) in the example compute node of FIG. 2 is anoperating system (162), a module of computer program instructions androutines for an application program's access to other resources of thecompute node. It is possible, in some embodiments at least, for anapplication program, an application messaging module, and a PAMI in acompute node of a parallel computer to run threads of execution with nouser login and no security issues because each such thread is entitledto complete access to all resources of the node. The quantity andcomplexity of duties to be performed by an operating system on a computenode in a parallel computer therefore can be somewhat smaller and lesscomplex than those of an operating system on a serial computer with manythreads running simultaneously with various level of authorization foraccess to resources. In addition, there is no video I/O on the computenode (152) of FIG. 2, another factor that decreases the demands on theoperating system. The operating system may therefore be quitelightweight by comparison with operating systems of general purposecomputers, a pared down or ‘lightweight’ version as it were, or anoperating system developed specifically for operations on a particularparallel computer. Operating systems that may be improved or simplifiedfor use in a compute node according to embodiments of the presentinvention include UNIX™, Linux™, Microsoft XP™, AIX™, IBM's i5/OS™, andothers as will occur to those of skill in the art.

The example compute node (152) of FIG. 2 includes several communicationsadapters (172, 176, 180, 188) for implementing data communications withother nodes of a parallel computer. Such data communications may becarried out serially through RS-232 connections, through external busessuch as USB, through data communications networks such as IP networks,and in other ways as will occur to those of skill in the art.Communications adapters implement the hardware level of datacommunications through which one computer sends data communications toanother computer, directly or through a network. Examples ofcommunications adapters for use in computers that implement internodedata communications according to embodiments of the present inventioninclude modems for wired communications, Ethernet (IEEE 802.3) adaptersfor wired network communications, and 802.11b adapters for wirelessnetwork communications.

The data communications adapters in the example of FIG. 2 include aGigabit Ethernet adapter (172) that couples example compute node (152)for data communications to a Gigabit Ethernet (174). Gigabit Ethernet isa network transmission standard, defined in the IEEE 802.3 standard,that provides a data rate of 1 billion bits per second (one gigabit).Gigabit Ethernet is a variant of Ethernet that operates over multimodefiber optic cable, single mode fiber optic cable, or unshielded twistedpair.

The data communications adapters in the example of FIG. 2 includes aJTAG Slave circuit (176) that couples example compute node (152) fordata communications to a JTAG Master circuit (178). JTAG is the usualname for the IEEE 1149.1 standard entitled Standard Test Access Port andBoundary-Scan Architecture for test access ports used for testingprinted circuit boards using boundary scan. JTAG is so widely adaptedthat, at this time, boundary scan is more or less synonymous with JTAG.JTAG is used not only for printed circuit boards, but also forconducting boundary scans of integrated circuits, and is also used as amechanism for debugging embedded systems. The example compute node ofFIG. 2 may be all three of these: It typically includes one or moreintegrated circuits installed on a printed circuit board and may beimplemented as an embedded system having its own processor, its ownmemory, and its own I/O capability. JTAG boundary scans through JTAGSlave (176) may efficiently configure processor registers and memory incompute node (152) for use in internode data communications according toembodiments of the present invention.

The data communications adapters in the example of FIG. 2 includes aPoint To Point Adapter (180) that couples example compute node (152) fordata communications to a data communications network (108) that isoptimal for point to point message passing operations such as, forexample, a network configured as a three-dimensional torus or mesh.Point To Point Adapter (180) provides data communications in sixdirections on three communications axes, x, y, and z, through sixbidirectional links: +x (181), −x (182), +y (183), −y (184), +z (185),and −z (186). For ease of explanation, the Point To Point Adapter (180)of FIG. 2 as illustrated is configured for data communications in threedimensions, x, y, and z, but readers will recognize that Point To PointAdapters optimized for point-to-point operations in internode datacommunications in a parallel computer according to embodiments of thepresent invention may in fact be implemented so as to supportcommunications in two dimensions, four dimensions, five dimensions, andso on.

The data communications adapters in the example of FIG. 2 includes aCollective Operations Adapter (188) that couples example compute node(152) for data communications to a network (106) that is optimal forcollective message passing operations such as, for example, a networkconfigured as a binary tree. Collective Operations Adapter (188)provides data communications through three bidirectional links: two tochildren nodes (190) and one to a parent node (192).

The example compute node (152) includes a number of arithmetic logicunits (‘ALUs’). ALUs (166) are components of processors (164), and aseparate ALU (170) is dedicated to the exclusive use of collectiveoperations adapter (188) for use in performing the arithmetic andlogical functions of reduction operations. Computer program instructionsof a reduction routine in an application messaging module (216) or aPAMI (218) may latch an instruction for an arithmetic or logicalfunction into instruction register (169). When the arithmetic or logicalfunction of a reduction operation is a ‘sum’ or a ‘logical OR,’ forexample, collective operations adapter (188) may execute the arithmeticor logical operation by use of an ALU (166) in a processor (164) or,typically much faster, by use of the dedicated ALU (170).

The example compute node (152) of FIG. 2 includes a direct memory access(DMA') controller (225), a module of automated computing machinery thatimplements, through communications with other DMA engines on othercompute nodes, or on a same compute node, direct memory access to andfrom memory on its own compute node as well as memory on other computenodes. Direct memory access is a way of reading and writing to and frommemory of compute nodes with reduced operational burden on computerprocessors (164); a CPU initiates a DMA transfer, but the CPU does notexecute the DMA transfer. A DMA transfer essentially copies a block ofmemory from one compute node to another, or between RAM segments ofapplications on the same compute node, from an origin to a target for aPUT operation, from a target to an origin for a GET operation.

For further explanation, FIG. 3A illustrates an example of a Point ToPoint Adapter (180) useful in parallel computers that perform internodedata communications according to embodiments of the present invention.Point To Point Adapter (180) is designed for use in a datacommunications network optimized for point to point operations, anetwork that organizes compute nodes in a three-dimensional torus ormesh. Point To Point Adapter (180) in the example of FIG. 3A providesdata communication along an x-axis through four unidirectional datacommunications links, to and from the next node in the −x direction(182) and to and from the next node in the +x direction (181). Point ToPoint Adapter (180) also provides data communication along a y-axisthrough four unidirectional data communications links, to and from thenext node in the −y direction (184) and to and from the next node in the+y direction (183). Point To Point Adapter (180) in also provides datacommunication along a z-axis through four unidirectional datacommunications links, to and from the next node in the −z direction(186) and to and from the next node in the +z direction (185). For easeof explanation, the Point To Point Adapter (180) of FIG. 3A asillustrated is configured for data communications in only threedimensions, x, y, and z, but readers will recognize that Point To PointAdapters optimized for point-to-point operations in a parallel computerthat performs internode data communications according to embodiments ofthe present invention may in fact be implemented so as to supportcommunications in two dimensions, four dimensions, five dimensions, andso on. Several supercomputers now use five dimensional mesh or torusnetworks, including, for example, IBM's Blue Gene Q™.

For further explanation, FIG. 3B illustrates an example of a CollectiveOperations Adapter (188) useful in a parallel computer that performsinternode data communications according to embodiments of the presentinvention. Collective Operations Adapter (188) is designed for use in anetwork optimized for collective operations, a network that organizescompute nodes of a parallel computer in a binary tree. CollectiveOperations Adapter (188) in the example of FIG. 3B provides datacommunication to and from two children nodes through four unidirectionaldata communications links (190). Collective Operations Adapter (188)also provides data communication to and from a parent node through twounidirectional data communications links (192).

For further explanation, FIG. 4 sets forth a line drawing illustratingan example data communications network (108) optimized forpoint-to-point operations useful in parallel computers that performinternode data communications according to embodiments of the presentinvention. In the example of FIG. 4, dots represent compute nodes (102)of a parallel computer, and the dotted lines between the dots representdata communications links (103) between compute nodes. The datacommunications links are implemented with point-to-point datacommunications adapters similar to the one illustrated for example inFIG. 3A, with data communications links on three axis, x, y, and z, andto and fro in six directions +x (181), −x (182), +y (183), −y (184), +z(185), and −z (186). The links and compute nodes are organized by thisdata communications network optimized for point-to-point operations intoa three dimensional mesh (105). The mesh (105) has wrap-around links oneach axis that connect the outermost compute nodes in the mesh (105) onopposite sides of the mesh (105). These wrap-around links form a torus(107). Each compute node in the torus has a location in the torus thatis uniquely specified by a set of x, y, z coordinates. Readers will notethat the wrap-around links in the y and z directions have been omittedfor clarity, but are configured in a similar manner to the wrap-aroundlink illustrated in the x direction. For clarity of explanation, thedata communications network of FIG. 4 is illustrated with only 27compute nodes, but readers will recognize that a data communicationsnetwork optimized for point-to-point operations in a parallel computerthat performs internode data communications according to embodiments ofthe present invention may contain only a few compute nodes or maycontain thousands of compute nodes. For ease of explanation, the datacommunications network of FIG. 4 is illustrated with only threedimensions: x, y, and z, but readers will recognize that a datacommunications network optimized for point-to-point operations may infact be implemented in two dimensions, four dimensions, five dimensions,and so on. As mentioned, several supercomputers now use five dimensionalmesh or torus networks, including IBM's Blue Gene Q™.

For further explanation, FIG. 5 illustrates an example datacommunications network (106) optimized for collective operations byorganizing compute nodes in a tree. The example data communicationsnetwork of FIG. 5 includes data communications links connected to thecompute nodes so as to organize the compute nodes as a tree. In theexample of FIG. 5, dots represent compute nodes (102) of a parallelcomputer, and the dotted lines (103) between the dots represent datacommunications links between compute nodes. The data communicationslinks are implemented with collective operations data communicationsadapters similar to the one illustrated for example in FIG. 3B, witheach node typically providing data communications to and from twochildren nodes and data communications to and from a parent node, withsome exceptions. Nodes in a binary tree may be characterized as a rootnode (202), branch nodes (204), and leaf nodes (206). The root node(202) has two children but no parent. The leaf nodes (206) each has aparent, but leaf nodes have no children. The branch nodes (204) each hasboth a parent and two children. The links and compute nodes are therebyorganized by this data communications network optimized for collectiveoperations into a binary tree (106). For clarity of explanation, thedata communications network of FIG. 5 is illustrated with only 31compute nodes, but readers will recognize that a data communicationsnetwork optimized for collective operations for use in parallelcomputers that perform internode data communications according toembodiments of the present invention may contain only a few computenodes or hundreds or thousands of compute nodes.

In the example of FIG. 5, each node in the tree is assigned a unitidentifier referred to as a ‘rank’ (196). The rank actually identifiesan instance of a parallel application that is executing on a computenode. That is, the rank is an application-level identifier. Using therank to identify a node assumes that only one such instance of anapplication is executing on each node. A compute node can, however,support multiple processors, each of which can support multipleprocessing cores—so that more than one process or instance of anapplication can easily be present under execution on any given computenode—or in all the compute nodes, for that matter. To the extent thatmore than one instance of an application executes on a single computenode, the rank identifies the instance of the application as such ratherthan the compute node. A rank uniquely identifies an application'slocation in the tree network for use in both point-to-point andcollective operations in the tree network. The ranks in this example areassigned as integers beginning with ‘0’ assigned to the root instance orroot node (202), ‘1’ assigned to the first node in the second layer ofthe tree, ‘2’ assigned to the second node in the second layer of thetree, ‘3’ assigned to the first node in the third layer of the tree, ‘4’assigned to the second node in the third layer of the tree, and so on.For ease of illustration, only the ranks of the first three layers ofthe tree are shown here, but all compute nodes, or rather allapplication instances, in the tree network are assigned a unique rank.Such rank values can also be assigned as identifiers of applicationinstances as organized in a mesh or torus network.

For further explanation, FIG. 6 sets forth a block diagram of an exampleprotocol stack useful in parallel computers that perform internode datacommunications according to embodiments of the present invention. Theexample protocol stack of FIG. 6 includes a hardware layer (214), asystem messaging layer (212), an application messaging layer (210), andan application layer (208). For ease of explanation, the protocol layersin the example stack of FIG. 6 are shown connecting an origin computenode (222) and a target compute node (224), although it is worthwhile topoint out that in embodiments that effect DMA data transfers, the origincompute node and the target compute node can be the same compute node.

The granularity of connection through the system messaging layer (212),which is implemented with a PAMI (218), is finer than merely computenode to compute node—because, again, communications among endpointsoften is communications among endpoints on the same compute node. Forfurther explanation, recall that the PAMI (218) connects endpoints,connections specified by combinations of clients, contexts, and tasks,each such combination being specific to a thread of execution on acompute node, with each compute node capable of supporting many threadsand therefore many endpoints. Every endpoint typically can function asboth an origin endpoint or a target endpoint for data transfers througha PAMI, and both the origin endpoint and its target endpoint can belocated on the same compute node. So an origin compute node (222) andits target compute node (224) can in fact, and often will, be the samecompute node.

The application layer (208) provides communications among instances of aparallel application (158) running on the compute nodes (222, 224) byinvoking functions in an application messaging module (216) installed oneach compute node. Communications among instances of the applicationthrough messages passed between the instances of the application.Applications may communicate messages invoking function of anapplication programming interface (‘API’) exposed by the applicationmessaging module (216). In this approach, the application messagingmodule (216) exposes a traditional interface, such as an API of an MPIlibrary, to the application program (158) so that the applicationprogram can gain the benefits of a PAMI, reduced network traffic,callback functions, and so on, with no need to recode the application.Alternatively, if the parallel application is programmed to use PAMIfunctions, the application can call the PAMI functions directly, withoutgoing through the application messaging module.

The example protocol stack of FIG. 6 includes a system messaging layer(212) implemented here as a PAMI (218). The PAMI provides system-leveldata communications functions that support messaging in the applicationlayer (602) and the application messaging layer (610). Such system-levelfunctions are typically invoked through an API exposed to theapplication messaging modules (216) in the application messaging layer(210). Although developers can in fact access a PAMI API directly bycoding an application to do so, a PAMI's system-level functions in thesystem messaging layer (212) in many embodiments are isolated from theapplication layer (208) by the application messaging layer (210), makingthe application layer somewhat independent of system specific details.With an application messaging module presenting a standard MPI API to anapplication, for example, with the application messaging module retooledto use the PAMI to carry out the low-level messaging functions, theapplication gains the benefits of a PAMI with no need to incur theexpense of reprogramming the application to call the PAMI directly.Because, however, some applications will in fact be reprogrammed to callthe PAMI directly, all entities in the protocol stack above the PAMI areviewed by PAMI as applications. When PAMI functions are invoked byentities above the PAMI in the stack, the PAMI makes no distinctionwhether the caller is in the application layer or the applicationmessaging layer, no distinction whether the caller is an application assuch or an MPI library function invoked by an application. As far as thePAMI is concerned, any caller of a PAMI function is an application.

The protocol stack of FIG. 6 includes a hardware layer (634) thatdefines the physical implementation and the electrical implementation ofaspects of the hardware on the compute nodes such as the bus, networkcabling, connector types, physical data rates, data transmissionencoding and many other factors for communications between the computenodes (222) on the physical network medium. In parallel computers thatperform internode data communications according to embodiments of thepresent invention, the hardware layer includes DMA controllers andnetwork links, including routers, packet switches, and the like.

For further explanation, FIG. 7 sets forth a functional block diagram ofan example PAMI (218) for use in parallel computers that performinternode data communications according to embodiments of the presentinvention. The PAMI (218) provides an active messaging layer thatsupports both point to point communications in a mesh or torus as wellas collective operations, gathers, reductions, barriers, and the like intree networks, for example. The PAMI is a multithreaded parallelcommunications engine designed to provide low level message passingfunctions, many of which are one-sided, and abstract such functions forhigher level messaging middleware, referred to in this specification as‘application messaging modules’ in an application messaging layer. Inthe example of FIG. 7, the application messaging layer is represented bya generic MPI module (258), appropriate for ease of explanation becausesome form of MPI is a de facto standard for such messaging middleware.Compute nodes and communications endpoints of a parallel computer (102on FIG. 1) are coupled for data communications through such a PAMI andthrough data communications resources (294, 296, 314) that include DMAcontrollers, network adapters, and data communications networks throughwhich controllers and adapters deliver data communications. The PAMI(218) provides data communications among data communications endpoints,where each endpoint is specified by data communications parameters for athread of execution on a compute node, including specifications of aclient, a context, and a task.

The PAMI (218) in this example includes PAMI clients (302, 304), tasks(286, 298), contexts (190, 292, 310, 312), and endpoints (288, 300). APAMI client is a collection of data communications resources (294, 295,314) dedicated to the exclusive use of an application-level dataprocessing entity, an application or an application messaging modulesuch as an MPI library. Data communications resources assigned incollections to PAMI clients are explained in more detail below withreference to FIGS. 8A and 8B. PAMI clients (203, 304 on FIG. 7) enablehigher level middleware, application messaging modules, MPI libraries,and the like, to be developed independently so that each can be usedconcurrently by an application. Although the application messaging layerin FIG. 7 is represented for example by a single generic MPI module(258), in fact, a PAMI, operating multiple clients, can support multiplemessage passing libraries or application messaging modulessimultaneously, a fact that is explained in more detail with referenceto FIG. 9. FIG. 9 sets forth a functional block diagram of an examplePAMI (218) useful in parallel computers that perform internode datacommunications according to embodiments of the present invention inwhich the example PAMI operates, on behalf of an application (158), withmultiple application messaging modules (502-510) simultaneously. Theapplication (158) can have multiple messages in transit simultaneouslythrough each of the application messaging modules (502-510). Eachcontext (512-520) carries out, through post and advance functions, datacommunications for the application on data communications resources inthe exclusive possession, in each client, of that context. Each contextcarries out data communications operations independently and in parallelwith other contexts in the same or other clients. In the example FIG. 9,each client (532-540) includes a collection of data communicationsresources (522-530) dedicated to the exclusive use of anapplication-level data processing entity, one of the applicationmessaging modules (502-510):

-   -   IBM MPI Library (502) operates through context (512) data        communications resources (522) dedicated to the use of PAMI        client (532),    -   MPICH Library (504) operates through context (514) data        communications resources (524) dedicated to the use of PAMI        client (534),    -   Unified Parallel C (‘UPC’) Library (506) operates through        context (516) data communications resources (526) dedicated to        the use of PAMI client (536),    -   Partitioned Global Access Space (‘PGAS’) Runtime Library (508)        operates through context (518) data communications resources        (528) dedicated to the use of PAMI client (538), and    -   Aggregate Remote Memory Copy Interface (‘ARMCI’) Library (510)        operates through context (520) data communications resources        (530) dedicated to the use of PAMI client (540).

Again referring to the example of FIG. 7: The PAMI (218) includes tasks,listed in task lists (286, 298) and identified (250) to the application(158). A ‘task’ as the term is used in PAMI operations is aplatform-defined integer datatype that identifies a canonicalapplication process, an instance of a parallel application (158). Verycarefully in this specification, the term ‘task’ is always used to referonly to this PAMI structure, not the traditional use of the computerterm ‘task’ to refer to a process or thread of execution. In thisspecification, the term ‘process’ refers to a canonical data processingprocess, a container for threads in a multithreading environment. Inparticular in the example of FIG. 7, the application (158) isimplemented as a canonical process with multiple threads (251-254)assigned various duties by a leading thread (251) which itself executesan instance of a parallel application program. Each instance of aparallel application is assigned a task; each task so assigned can be aninteger value, for example, in a C environment, or a separate taskobject in a C++ or Java environment. The tasks are components ofcommunications endpoints, but are not themselves communicationsendpoints; tasks are not addressed directly for data communications inPAMI. This gives a finer grained control than was available in priormessage passing art. Each client has its own list (286, 298) of tasksfor which its contexts provide services; this allows each process topotentially reside simultaneously in two or more differentcommunications domains as will be the case in certain advanced computersusing, for example, one type of processor and network in one domain anda completely different processor type and network in another domain, allin the same computer.

The PAMI (218) includes contexts (290, 292, 310, 312). A ‘context’ asthe term is used in PAMI operations is composed of a subset of aclient's collection of data processing resources, context functions, anda work queue of data transfer instructions to be performed by use of thesubset through the context functions operated by an assigned thread ofexecution. That is, a context represents a partition of the local datacommunications resources assigned to a PAMI client. Every context withina client has equivalent functionality and semantics. Context functionsimplement contexts as threading points that applications use to optimizeconcurrent communications. Communications initiated by a local process,an instance of a parallel application, uses a context object to identifythe specific threading point that will be used to issue a particularcommunication independent of communications occurring in other contexts.In the example of FIG. 7, where the application (158) and theapplication messaging module (258) are both implemented as canonicalprocesses with multiple threads of execution, each has assigned ormapped particular threads (253, 254, 262, 264) to advance (268, 270,276, 278) work on the contexts (290, 292, 310, 312), including executionof local callbacks (272, 280). In particular, the local event callbackfunctions (272, 280) associated with any particular communication areinvoked by the thread advancing the context that was used to initiatethe communication operation in the first place. Like PAMI tasks,contexts are not used to directly address a communication destination ortarget, as they are a local resource.

Context functions, explained here with regard to references (472-482) onFIG. 9, include functions to create (472) and destroy (474) contexts,functions to lock (476) and unlock (478) access to a context, andfunctions to post (480) and advance (480) work in a context. For ease ofexplanation, the context functions (472-482) are illustrated in only oneexpanded context (512); readers will understand, however, that all PAMIcontexts have similar context functions. The create (472) and destroy(474) functions are, in an object-oriented sense, constructors anddestructors. In the example embodiments described in thisspecifications, post (480) and advance (482) functions on a context arecritical sections, not thread safe. Applications using suchnon-reentrant functions must somehow ensure that critical sections areprotected from re-entrant use. Applications can use mutual exclusionlocks to protect critical sections. The lock (476) and unlock (478)functions in the example of FIG. 9 provide and operate such a mutualexclusion lock to protect the critical sections in the post (480) andadvance (482) functions. If only a single thread posts or advances workon a context, then that thread need never lock that context. To theextent that progress is driven independently on a context by a singlethread of execution, then no mutual exclusion locking of the contextitself is required—provided that no other thread ever attempts to call afunction on such a context. If more than one thread will post or advancework on a context, each such thread must secure a lock before calling apost or an advance function on that context. This is one reason why itis probably a preferred architecture, given sufficient resources, toassign one thread to operate each context. Progress can be driven withadvance (482) functions concurrently among multiple contexts by usingmultiple threads, as desired by an application—shown in the example ofFIG. 7 by threads (253, 254, 262, 264) which advance work concurrently,independently and in parallel, on contexts (290, 292, 310, 312).

Posts and advances (480, 482 on FIG. 9) are functions called on acontext, either in a C-type function with a context ID as a parameter,or in object oriented practice where the calling entity possesses areference to a context or a context object as such and the posts andadvances are member methods of a context object. Again referring to FIG.7: Application-level entities, application programs (158) andapplication messaging modules (258), post (266, 274) data communicationsinstructions, including SENDs, RECEIVEs, PUTs, GETs, and so on, to thework queues (282, 284, 306, 308) in contexts and then call advancefunctions (268, 270, 276, 278) on the contexts to progress specific dataprocessing and data communications that carry out the instructions. Thedata processing and data communications effected by the advancefunctions include specific messages, request to send (‘RTS’) messages,acknowledgments, callback execution, transfers of transfer data orpayload data, and so on. Advance functions therefore operate generallyby checking a work queue for any new instructions that need to beinitiated and checking data communications resources for any incomingmessage traffic that needs to be administered as well as increases instorage space available for outgoing message traffic, with callbacks andthe like. Advance functions also carry out or trigger transfers oftransfer data or payload data.

In at least some embodiments, a context's subset of a client's dataprocessing resources is dedicated to the exclusive use of the context.In the example of FIG. 7, context (290) has a subset (294) of a client's(302) data processing resources dedicated to the exclusive use of thecontext (290), and context (292) has a subset (296) of a client's (302)data processing resources dedicated to the exclusive use of the context(292). Advance functions (268, 270) called on contexts (290, 292)therefore never need to secure a lock on a data communications resourcebefore progressing work on a context—because each context (290, 292) hasexclusive use of dedicated data communications resources. Usage of datacommunications resources in this example PAMI (218), however, is notthread-safe. When data communications resources are shared amongcontexts, mutual exclusion locks are needed. In contrast to theexclusive usage of resources by contexts (290, 292), contexts (310, 312)share access to their client's data communications resource (314) andtherefore do not have data communications resources dedicated toexclusive use of a single context. Contexts (310, 312) therefore alwaysmust secure a mutual exclusion lock on a data communications resourcebefore using the resource to send or receive administrative messages ortransfer data.

For further explanation, here is an example pseudocode Hello Worldprogram for an application using a PAMI:

int main(int argc, char ** argv) {   PAMI_client_t client;  PAMI_context_t context;   PAMI_result_t status = PAMI_ERROR;   constchar *name = “PAMI”;   status = PAMI_Client_initialize(name, &client);  size_t_n = 1;   status = PAMI_Context_createv(client, NULL, 0,&context, _n);   PAMI_configuration_t configuration;  configuration.name = PAMI_TASK_ID;   status =PAMI_Configuration_query(client, &configuration);   size_t task_id =configuration.value.intval;   configuration.name = PAMI_NUM_TASKS;  status = PAMI_Configuration_query(client, &configuration);   size_tnum_tasks = configuration.value.intval;   fprintf (stderr, “Helloprocess %d of %d\n”, task_id, num_tasks);   status =PAMI_Context_destroy(context);   status = PAMI_Client_finalize(client);  return 0; }

This short program is termed ‘pseudocode’ because it is an explanationin the form of computer code, not a working model, not an actual programfor execution. In this pseudocode example, an application initializes aclient and a context for an application named “PAMI.”PAMI_Client_initialize and PAMI_Context_createv are initializationfunctions (316) exposed to applications as part of a PAMI's API. Thesefunctions, in dependence upon the application name “PAMI,” pull from aPAMI configuration (318) the information needed to establish a clientand a context for the application. The application uses this segment:

PAMI_configuration_t configuration; configuration.name = PAMI_TASK_ID;status = PAMI_Configuration_query(client, &configuration); size_ttask_id = configuration.value.intval;to retrieve its task ID and this segment:

configuration.name = PAMI_NUM_TASKS; status =PAMI_Configuration_query(client, &configuration); size_t num_tasks =configuration.value.intval;to retrieve the number of tasks presently configured to carry outparallel communications and process data communications event in thePAMI. The applications prints “Hello process task_id of num_tasks,”where task_id is the task ID of the subject instance of a parallelapplication, and num_tasks is the number of instances of the applicationexecuting in parallel on compute nodes. Finally, the applicationdestroys the context and terminates the client.

For further explanation of data communications resources assigned incollections to PAMI clients, FIG. 8A sets forth a block diagram ofexample data communications resources (220) useful in parallel computersthat perform internode data communications according to embodiments ofthe present invention. The data communications resources of FIG. 8Ainclude a gigabit Ethernet adapter (238), an Infiniband adapter (240), aFibre Channel adapter (242), a PCI Express adapter (246), a collectiveoperations network configured as a tree (106), shared memory (227), DMAcontrollers (225, 226), and a network (108) configured as apoint-to-point torus or mesh like the network described above withreference to FIG. 4. A PAMI is configured with clients, each of which isin turn configured with certain collections of such data communicationsresources—so that, for example, the PAMI client (302) in the PAMI (218)in the example of FIG. 7 can have dedicated to its use a collection ofdata communications resources composed of six segments (227) of sharedmemory, six Gigabit Ethernet adapters (238), and six Infiniband adapters(240). And the PAMI client (304) can have dedicated to its use six FibreChannel adapters (242), a DMA controller (225), a torus network (108),and five segments (227) of shared memory. And so on.

The DMA controllers (225, 226) in the example of FIG. 8A each isconfigured with DMA control logic in the form of a DMA engine (228,229), an injection FIFO buffer (230), and a receive FIFO buffer (232).The DMA engines (228, 229) can be implemented as hardware components,logic networks of a DMA controller, in firmware, as software operatingan embedded controller, as various combinations of software, firmware,or hardware, and so on. Each DMA engine (228, 229) operates on behalf ofendpoints to send and receive DMA transfer data through the network(108). The DMA engines (228, 229) operate the injection buffers (230,232) by processing first-in-first-out descriptors (234, 236) in thebuffers, hence the designation ‘injection FIFO’ and ‘receive FIFO.’

For further explanation, here is an example use case, a description ofthe overall operation of an example PUT DMA transfer using the DMAcontrollers (225, 226) and network (108) in the example of FIG. 8A: Anoriginating application (158), which is typically one instance of aparallel application running on a compute node, places a quantity oftransfer data (494) at a location in its RAM (155). The application(158) then calls a post function (480) on a context (512) of an originendpoint (352), posting a PUT instruction (390) into a work queue (282)of the context (512); the PUT instruction (390) specifies a targetendpoint (354) to which the transfer data is to be sent as well assource and destination memory locations. The application then calls anadvance function (482) on the context (512). The advance function (482)finds the new PUT instruction in its work queue (282) and inserts a datadescriptor (234) into the injection FIFO of the origin DMA controller(225); the data descriptor includes the source and destination memorylocations and the specification of the target endpoint. The origin DMAengine (225) then transfers the data descriptor (234) as well as thetransfer data (494) through the network (108) to the DMA controller(226) on the target side of the transaction. The target DMA engine(229), upon receiving the data descriptor and the transfer data, placesthe transfer data (494) into the RAM (156) of the target application atthe location specified in the data descriptor and inserts into thetarget DMA controller's receive FIFO (232) a data descriptor (236) thatspecifies the target endpoint and the location of the transfer data(494) in RAM (156). The target application (159) or application instancecalls an advance function (483) on a context (513) of the targetendpoint (354). The advance function (483) checks the communicationsresources assigned to its context (513) for incoming messages, includingchecking the receive FIFO (232) of the target DMA controller (226) fordata descriptors that specify the target endpoint (354). The advancefunction (483) finds the data descriptor for the PUT transfer andadvises the target application (159) that its transfer data has arrived.A GET-type DMA transfer works in a similar manner, with somedifferences, including, of course, the fact that transfer data flows inthe opposite direction. Similarly, typical SEND transfers also operatesimilarly, some with rendezvous protocols, some with eager protocols,with data transmitted in packets over the a network through non-DMAnetwork adapters or through DMA controllers.

The example of FIG. 8A includes two DMA controllers (225, 226). DMAtransfers between endpoints on separate compute nodes use two DMAcontrollers, one on each compute node. Compute nodes can be implementedwith multiple DMA controllers so that many or even all DMA transferseven among endpoints on a same compute node can be carried out using twoDMA engines. In some embodiments at least, however, a compute node, likethe example compute node (152) of FIG. 2, has only one DMA engine, sothat that DMA engine can be use to conduct both sides of transfersbetween endpoints on that compute node. For further explanation of thisfact, FIG. 8B sets forth a functional block diagram of an example DMAcontroller (225) operatively coupled to a network (108)—in anarchitecture where this DMA controller (225) is the only DMA controlleron a compute node—and an origin endpoint (352) and its target endpoint(354) are both located on the same compute node (152). In the example ofFIG. 8B, a single DMA engine (228) operates with two threads ofexecution (502, 504) on behalf of endpoints (352, 354) on a same computenode to send and receive DMA transfer data through a segment (227) ofshared memory. A transmit thread (502) injects transfer data into thenetwork (108) as specified in data descriptors (234) in an injectionFIFO buffer (230), and a receive thread (502) receives transfer datafrom the network (108) as specified in data descriptors (236) in areceive FIFO buffer (232).

The overall operation of an example PUT DMA transfer with the DMAcontrollers (225) and the network (108) in the example of FIG. 8B is: Anoriginating application (158), that is actually one of multipleinstances (158, 159) of a parallel application running on a compute node(152) in separate threads of execution, places a quantity of transferdata (494) at a location in its RAM (155). The application (158) thencalls a post function (480) on a context (512) of an origin endpoint(352), posting a PUT instruction (390) into a work queue (282) of thecontext (512); the PUT instruction specifies a target endpoint (354) towhich the transfer data is to be sent as well as source and destinationmemory locations. The application (158) then calls an advance function(482) on the context (512). The advance function (482) finds the new PUTinstruction (390) in its work queue (282) and inserts a data descriptor(234) into the injection FIFO of the DMA controller (225); the datadescriptor includes the source and destination memory locations and thespecification of the target endpoint. The DMA engine (225) thentransfers by its transmit and receive threads (502, 504) through thenetwork (108) the data descriptor (234) as well as the transfer data(494). The DMA engine (228), upon receiving by its receive thread (504)the data descriptor and the transfer data, places the transfer data(494) into the RAM (156) of the target application and inserts into theDMA controller's receive FIFO (232) a data descriptor (236) thatspecifies the target endpoint and the location of the transfer data(494) in RAM (156). The target application (159) calls an advancefunction (483) on a context (513) of the target endpoint (354). Theadvance function (483) checks the communications resources assigned toits context for incoming messages, including checking the receive FIFO(232) of the DMA controller (225) for data descriptors that specify thetarget endpoint (354). The advance function (483) finds the datadescriptor for the PUT transfer and advises the target application (159)that its transfer data has arrived. Again, a GET-type DMA transfer worksin a similar manner, with some differences, including, of course, thefact that transfer data flows in the opposite direction. And typicalSEND transfers also operate similarly, some with rendezvous protocols,some with eager protocols, with data transmitted in packets over the anetwork through non-DMA network adapters or through DMA controllers.

By use of an architecture like that illustrated and described withreference to FIG. 8B, a parallel application or an application messagingmodule that is already programmed to use DMA transfers can gain thebenefit of the speed of DMA data transfers among endpoints on the samecompute node with no need to reprogram the applications or theapplication messaging modules to use the network in other modes. In thisway, an application or an application messaging module, alreadyprogrammed for DMA, can use the same DMA calls through a same API forDMA regardless whether subject endpoints are on the same compute node oron separate compute nodes.

For further explanation, FIG. 10 sets forth a functional block diagramof example endpoints useful in parallel computers that perform internodedata communications according to embodiments of the present invention.In the example of FIG. 10, a PAMI (218) is implemented with instances ontwo separate compute nodes (152, 153) that include four endpoints (338,340, 342, 344). These endpoints are opaque objects used to address anorigin or destination in a process and are constructed from a (client,task, context) tuple. Non-DMA SEND and RECEIVE instructions as well asDMA instructions such as PUT and GET address a destination by use of anendpoint object or endpoint identifier.

Each endpoint (338, 340, 342, 344) in the example of FIG. 10 is composedof a client (302, 303, 304, 305), a task (332, 333, 334, 335), and acontext (290, 292, 310, 312). Using a client a component in thespecification of an endpoint disambiguates the task and contextidentifiers, as these identifiers may be the same for multiple clients.A task is used as a component in the specification of an endpoint toconstruct an endpoint to address a process accessible through a context.A context in the specification of an endpoint identifies, refers to, orrepresents the specific context associated with a destination or targettask—because the context identifies a specific threading point on atask. A context offset identifies which threading point is to process aparticular communications operation. Endpoints enable “crosstalk” whichis the act of issuing communication on a local context with a particularcontext offset that is directed to a destination endpoint with nocorrespondence to a source context or source context offset.

For efficient utilization of storage in an environment where multipletasks of a client reside on the same physical compute node, anapplication may choose to write an endpoint table (288, 300 on FIG. 7)in a segment of shared memory (227, 346, 348). It is the responsibilityof the application to allocate such segments of shared memory andcoordinate the initialization and access of any data structures sharedbetween processes. This includes any endpoint objects which are createdby one process or instance of an application and read by anotherprocess.

Endpoints (342, 344) on compute node (153) serve respectively twoapplication instances (157, 159). The tasks (334, 336) in endpoints(342, 344) are different. The task (334) in endpoint (342) is identifiedby the task ID (249) of application (157), and the task (336) inendpoint (344) is identified by the task ID (257) of application (159).The clients (304, 305) in endpoints (342, 344) are different, separateclients. Client (304) in endpoint (342) associates data communicationsresources (e.g., 294, 296, 314 on FIG. 7) dedicated exclusively to theuse of application (157), while client (305) in endpoint (344)associates data communications resources dedicated exclusively to theuse of application (159). Contexts (310, 312) in endpoints (342, 344)are different, separate contexts. Context (310) in endpoint (342)operates on behalf of application (157) a subset of the datacommunications resources of client (304), and context (312) in endpoint(344) operates on behalf of application (159) a subset of the datacommunications resources of client (305).

Contrasted with the PAMIs (218) on compute node (153), the PAMI (218) oncompute node (152) serves only one instance of a parallel application(158) with two endpoints (338, 340). The tasks (332, 333) in endpoints(338, 340) are the same, because they both represent a same instance ofa same application (158); both tasks (332,333) therefore are identified,either with a same variable value, references to a same object, or thelike, by the task ID (250) of application (158). The clients (302, 303)in endpoints (338, 340) are optionally either different, separateclients or the same client. If they are different, each associates aseparate collection of data communications resources. If they are thesame, then each client (302, 303) in the PAMI (218) on compute node(152) associates a same set of data communications resources and isidentified with a same value, object reference, or the like. Contexts(290, 292) in endpoints (338, 340) are different, separate contexts.Context (290) in endpoint (338) operates on behalf of application (158)a subset of the data communications resources of client (302) regardlesswhether clients (302, 303) are the same client or different clients, andcontext (292) in endpoint (340) operates on behalf of application (158)a subset of the data communications resources of client (303) regardlesswhether clients (302, 303) are the same client or different clients.Thus the tasks (332, 333) are the same; the clients (302, 303) can bethe same; and the endpoints (338, 340) are distinguished at least bydifferent contexts (290, 292), each of which operates on behalf of oneof the threads (251-254) of application (158), identified typically by acontext offset or a threading point.

Endpoints (338, 340) being as they are on the same compute node (152)can effect DMA data transfers between endpoints (338, 340) through DMAcontroller (225) and a segment of shared local memory (227). In theabsence of such shared memory (227), endpoints (338, 340) can effect DMAdata transfers through the DMA controller (225) and the network (108),even though both endpoints (338, 340) are on the same compute node(152). DMA transfers between endpoint (340) on compute node (152) andendpoint (344) on another compute node (153) go through DMA controllers(225, 226) and either a network (108) or a segment of shared remotememory (346). DMA transfers between endpoint (338) on compute node (152)and endpoint (342) on another compute node (153) also go through DMAcontrollers (225, 226) and either a network (108) or a segment of sharedremote memory (346). The segment of shared remote memory (346) is acomponent of a Non-Uniform Memory Access (‘NUMA’) architecture, asegment in a memory module installed anywhere in the architecture of aparallel computer except on a local compute node. The segment of sharedremote memory (346) is ‘remote’ in the sense that it is not installed ona local compute node. A local compute node is ‘local’ to the endpointslocated on that particular compute node. The segment of shared remotememory (346), therefore, is ‘remote’ with respect to endpoints (338,340) on compute node (158) if it is in a memory module on compute node(153) or anywhere else in the same parallel computer except on computenode (158).

Endpoints (342, 344) being as they are on the same compute node (153)can effect DMA data transfers between endpoints (342, 344) through DMAcontroller (226) and a segment of shared local memory (348). In theabsence of such shared memory (348), endpoints (342, 344) can effect DMAdata transfers through the DMA controller (226) and the network (108),even though both endpoints (342, 344) are on the same compute node(153). DMA transfers between endpoint (344) on compute node (153) andendpoint (340) on another compute node (152) go through DMA controllers(226, 225) and either a network (108) or a segment of shared remotememory (346). DMA transfers between endpoint (342) on compute node (153)and endpoint (338) on another compute node (158) go through DMAcontrollers (226, 225) and either a network (108) or a segment of sharedremote memory (346). Again, the segment of shared remote memory (346) is‘remote’ with respect to endpoints (342, 344) on compute node (153) ifit is in a memory module on compute node (158) or anywhere else in thesame parallel computer except on compute node (153).

For further explanation, FIG. 11 sets forth a flow chart illustrating anexample method of internode data communications in a parallel computeraccording to embodiments of the present invention. The method of FIG. 11is carried out in a parallel computer similar to the example parallelcomputer (100) of FIG. 1. Such a parallel computer (100) includes aplurality of compute nodes. Each compute node is configured to execute aplurality of processes. Each compute node includes a messaging unit andmain computer memory. The messaging unit of each compute node includescomputer memory separate from the main computer memory and the messagingunit couples the compute node for data communications to other computenodes.

The method of FIG. 11 include booting (602) each compute node andallocating (604), at boot time, by the messaging unit in the messagingunit's computer memory, a predefined number of message buffers. Eachmessage buffer is associated with a process to be initialized on thecompute node. Allocating (604) a predefined number of message buffersmay be carried out by allocating all available messaging unit memoryinto buffers of a predefined size, by allocating an exact number ofbuffers in the available messaging unit memory, and so on.

The method of FIG. 11 also includes receiving (606), by the messagingunit prior to initialization of a particular process on the computenode, a data communications message intended for the particular processand storing (610) the data communications message in the message bufferassociated with the particular process. The messaging unit may receive(606) the data communications message from another process on adifferent compute node. That is, the data communications message is aform of internode data communications.

The method of FIG. 11 continues by initializing (612) the particularprocess, establishing (614), by the particular process, a messagingbuffer in main memory of the compute node and copying (616) the datacommunications message from the message buffer of the messaging unitinto the message buffer of main memory. Once copied into the main memorymessage buffer, the particular process may process the datacommunications message as necessary.

Although the method of FIG. 11 includes initializing the particularprocess, in some instances, that process may not initialize. Forexample, the particular process may fail initialization due to asoftware or hardware related error or may stall during initialization,without ever completing the initialization process. In such examples,the particular process will not retrieve the data communications messagefrom the particular process's message buffer. To ensure completeddelivery of data communications in such a system then, the first processmay be configured to periodically poll, for a predefined amount of time,the particular process's message buffer to determine whether the datacommunications message has been retrieved by the particular process. Ifthe data communications message has not been retrieved during thepredefined amount of time, the first process may flush the particularprocess's message buffer (overwriting the data communications message)and send the data communications message to another process. Readers ofskill in the art will recognize, that polling the particular process'smessage buffer for a predefined amount of time is but one way, amongmany possible ways, to ensure delivery of a data communications messageto an uninitialized process. In another example, processes may beconfigured to send acknowledgments of receipt of data communicationsmessage. In such an example, a first process may be configured to sendthe data communications message to a particular process, wait foracknowledgement from the particular process for a predefined amount oftime, and send the data communications message to another process if thefirst process does not receive an acknowledgement from the particularprocess after the predefined amount of time.

The method of FIG. 11 may be implemented in a parallel computer thatincludes a PAMI (218). In such a parallel computer, the compute nodesmay execute a parallel application, and the PAMI may include datacommunications endpoints, with each endpoint including a specificationof data communications parameters for a thread of execution on a computenode, including specifications of a client, a context, and a task. Insuch and embodiment the endpoints may be coupled for data communicationsthrough the PAMI. The particular process described in FIG. 11 may beimplemented in this example embodiment as PAMI endpoints.

For further explanation, FIG. 12 sets forth a flow chart illustrating afurther example method of internode data communications in a parallelcomputer according to embodiments of the present invention. The methodof FIG. 12 is carried out in a parallel computer similar to the exampleparallel computer (100) of FIG. 1. Such a parallel computer (100)includes a plurality of compute nodes. Each compute node is configuredto execute a plurality of processes. Each compute node includes amessaging unit and main computer memory. The messaging unit of eachcompute node includes computer memory separate from the main computermemory and the messaging unit couples the compute node for datacommunications to other compute nodes.

The method of FIG. 12 is similar to the method of FIG. 11 in that themethod of FIG. 12 includes booting (602) a compute node; allocating(604), in messaging unit memory, a predefined number of message buffers;receiving (606), prior to initialization of a particular process, a datacommunications message intended for the particular process; storing(610) the message in the message buffer associated with the particularprocess; and, upon initialization (612) of the particular process,establishing (614), by the particular process, a messaging buffer inmain memory of the compute node; and copying (616), by the particularprocess, the data communications message from the message buffer of themessaging unit into the message buffer of main memory.

The method of FIG. 12 differs from the method of FIG. 11, however, inthat the method of FIG. 12 includes maintaining (1202), by eachmessaging unit, a status of unused message buffers in the messagingunit's memory.

Maintaining (1202), by each messaging unit, a status of unused messagebuffers in the messaging unit's memory may be carried out by settingflags for used message buffers and clearing flags for unused messagebuffers. Such flags may be implemented, for example, as a bit in abitstream stored at a predefined location.

The method of FIG. 12 also includes requesting (1204), by a process on acompute node via a broadcast operation, availability of messaging unitmessage buffers. The broadcast operation is an example of globaloperation in which data originates at a single node and is broadcast toall other nodes. Here, the data broadcast, is a request for availablemessage buffers. Such available message buffers may process availablefor on-demand execution. An on-demand process is a process initializedat the behest of another process, for a specified time or task.On-demand processes may be thought of accelerators, employed only whennecessary and available.

The method of FIG. 12 also includes receiving (1206), from eachmessaging unit, an indication of available messaging unit messagebuffers and sending (1208), by the process, a data communicationsmessage to an available messaging unit message buffer with aninstruction to initialize an on-demand process associated with thatmessaging unit message buffer. In this way, a process may be initializedand executed, on-demand, at a compute node where resources areavailable. Further, by bundling the request for initialization of theon-demand process with a data communications message intended for theon-demand process, efficiency of execution is increased. Here, theprocess requesting initialization of the on-demand process, need notwait for the on-demand process to be initialized. Instead, therequesting process may request initialization with a data communicationsmessage intended for the on-demand process and, without regard of thestatus of the on-demand process's initialization, continue execution. Inthis way, on-demand processes may be brought up and taken down—startedand stopped—efficiently. Other processes may call upon the on-demandprocess when in need, and without waiting for the on-demand process tobe initialized, send the on-demand process data to be processed.

For further explanation, FIG. 13 sets forth a flow chart illustrating afurther example method of internode data communications in a parallelcomputer according to embodiments of the present invention. The methodof FIG. 13 is carried out in a parallel computer similar to the exampleparallel computer (100) of FIG. 1. Such a parallel computer (100)includes a plurality of compute nodes. Each compute node is configuredto execute a plurality of processes. Each compute node includes amessaging unit and main computer memory. The messaging unit of eachcompute node includes computer memory separate from the main computermemory and the messaging unit couples the compute node for datacommunications to other compute nodes.

The method of FIG. 13 is similar to the method of FIG. 11 in that themethod of FIG. 13 includes booting (602) a compute node; allocating(604), in messaging unit memory, a predefined number of message buffers;receiving (606), prior to initialization of a particular process, a datacommunications message intended for the particular process; storing(610) the message in the message buffer associated with the particularprocess; and, upon initialization (612) of the particular process,establishing (614), by the particular process, a messaging buffer inmain memory of the compute node; and copying (616), by the particularprocess, the data communications message from the message buffer of themessaging unit into the message buffer of main memory.

The method of FIG. 13 differs from the method of FIG. 11, however, inthat the method of FIG. 13 includes receiving (1302), by a messagingunit, a request to initialize an on-demand process to execute a task. Inthe method of FIG. 13, receiving (1302) the request to initialize anon-demand process to execute a task includes receiving a datacommunications message intended for the on-demand process.

The method of FIG. 13 also includes storing (1304), by the messagingunit in the messaging unit's computer memory, the data communicationsmessage intended for the on-demand process in an available messagebuffer. Storing (1304) the data communications message intended for theon-demand process in an available message buffer may be carried out invarious ways including, for example, by inspecting a header of the datacommunications message for an identifier of the intended recipient andutilizing the identifier as (or to calculate) an offset from apredefined memory location, where the offset specifies the messagebuffer for the intended recipient.

The method of FIG. 13 also includes initializing (1306) the on-demandprocess. Initializing (1306) the on-demand process may be carried out invarious ways. For example, the messaging unit may be configured tointerrupt a PAMI or application when a request for an on-demand processis received. As another example, the messaging unit may be configured tostore a value indicating such a request in a predefined memory locationthat is polled periodically by a PAMI, a parallel application, anoperating system, or other module. Once it is determined that a processis to be initialized, the module responsible for such a task may fork aprocess into a parent and child process, where the child process is theon-demand process.

The method of FIG. 13 also includes establishing (1308), by theon-demand process upon initialization, a messaging buffer in main memoryof the compute node, copying (1310), by the on-demand process, the datacommunications message from the message buffer of the messaging unitinto the message buffer of main memory; and upon completion of theon-demand process's task, releasing (1312), by the on-demand process,the message buffer in the main memory and the message buffer in themessaging unit. Releasing the message buffer may be carried out invarious ways including for example, through a callback, during an exit,with a direct instruction to free the allocated memory, and in otherways as will occur to readers of skill in the art.

As will be appreciated by one skilled in the art, aspects of the presentinvention may be embodied as a system, method or computer programproduct. Accordingly, aspects of the present invention may take the formof an entirely hardware embodiment, an entirely software embodiment(including firmware, resident software, micro-code, etc.) or anembodiment combining software and hardware aspects that may allgenerally be referred to herein as a “circuit,” “module” or “system.”Furthermore, aspects of the present invention may take the form of acomputer program product embodied in one or more computer readablemedium(s) having computer readable program code embodied thereon.

Any combination of one or more computer readable medium(s) may beutilized. The computer readable medium may be a computer readable signalmedium or a computer readable storage medium. A computer readablestorage medium may be, for example, but not limited to, an electronic,magnetic, optical, electromagnetic, infrared, or semiconductor system,apparatus, or device, or any suitable combination of the foregoing. Morespecific examples (a non-exhaustive list) of the computer readablestorage medium would include the following: an electrical connectionhaving one or more wires, a portable computer diskette, a hard disk, arandom access memory (RAM), a read-only memory (ROM), an erasableprogrammable read-only memory (EPROM or Flash memory), an optical fiber,a portable compact disc read-only memory (CD-ROM), an optical storagedevice, a magnetic storage device, or any suitable combination of theforegoing. In the context of this document, a computer readable storagemedium may be any tangible medium that can contain, or store a programfor use by or in connection with an instruction execution system,apparatus, or device.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including, but not limited to,electro-magnetic, optical, or any suitable combination thereof. Acomputer readable signal medium may be any computer readable medium thatis not a computer readable storage medium and that can communicate,propagate, or transport a program for use by or in connection with aninstruction execution system, apparatus, or device.

Program code embodied on a computer readable medium may be transmittedusing any appropriate medium, including but not limited to wireless,wireline, optical fiber cable, RF, etc., or any suitable combination ofthe foregoing.

Computer program code for carrying out operations for aspects of thepresent invention may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java, Smalltalk, C++ or the like and conventional proceduralprogramming languages, such as the “C” programming language or similarprogramming languages. The program code may execute entirely on theuser's computer, partly on the user's computer, as a stand-alonesoftware package, partly on the user's computer and partly on a remotecomputer or entirely on the remote computer or server. In the latterscenario, the remote computer may be connected to the user's computerthrough any type of network, including a local area network (LAN) or awide area network (WAN), or the connection may be made to an externalcomputer (for example, through the Internet using an Internet ServiceProvider).

Aspects of the present invention are described above with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems) and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer program instructions. These computer program instructions maybe provided to a processor of a general purpose computer, specialpurpose computer, or other programmable data processing apparatus toproduce a machine, such that the instructions, which execute via theprocessor of the computer or other programmable data processingapparatus, create means for implementing the functions/acts specified inthe flowchart and/or block diagram block or blocks.

These computer program instructions may also be stored in a computerreadable medium that can direct a computer, other programmable dataprocessing apparatus, or other devices to function in a particularmanner, such that the instructions stored in the computer readablemedium produce an article of manufacture including instructions whichimplement the function/act specified in the flowchart and/or blockdiagram block or blocks.

The computer program instructions may also be loaded onto a computer,other programmable data processing apparatus, or other devices to causea series of operational steps to be performed on the computer, otherprogrammable apparatus or other devices to produce a computerimplemented process such that the instructions which execute on thecomputer or other programmable apparatus provide processes forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof code, which comprises one or more executable instructions forimplementing the specified logical function(s). It should also be notedthat, in some alternative implementations, the functions noted in theblock may occur out of the order noted in the figures. For example, twoblocks shown in succession may, in fact, be executed substantiallyconcurrently, or the blocks may sometimes be executed in the reverseorder, depending upon the functionality involved. It will also be notedthat each block of the block diagrams and/or flowchart illustration, andcombinations of blocks in the block diagrams and/or flowchartillustration, can be implemented by special purpose hardware-basedsystems that perform the specified functions or acts, or combinations ofspecial purpose hardware and computer instructions.

It will be understood from the foregoing description that modificationsand changes may be made in various embodiments of the present inventionwithout departing from its true spirit. The descriptions in thisspecification are for purposes of illustration only and are not to beconstrued in a limiting sense. The scope of the present invention islimited only by the language of the following claims.

1-7. (canceled)
 8. An apparatus for internode data communications in aparallel computer, the parallel computer comprising a plurality ofcompute nodes, each compute node comprising main computer memory and amessaging unit, the messaging unit comprising computer memory, themessaging unit comprising a module of automated computing machinerycoupling compute nodes for data communications, the apparatus comprisinga computer processor, a computer memory operatively coupled to thecomputer processor, the computer memory having disposed within itcomputer program instructions that, when executed, cause the apparatusto carry out the steps of: for each compute node at compute node boottime: allocating, by the messaging unit in the messaging unit's computermemory, a predefined number of message buffers, each message bufferassociated with a process to be initialized on the compute node;receiving, by the messaging unit prior to initialization of a particularprocess on the compute node, a data communications message intended forthe particular process; storing the data communications message in themessage buffer associated with the particular process; uponinitialization of the particular process, establishing, by theparticular process, a messaging buffer in main memory of the computenode; and copying, by the particular process, the data communicationsmessage from the message buffer of the messaging unit into the messagebuffer of main memory.
 9. The apparatus of claim 8 further comprisingcomputer program instructions that, when executed, cause the apparatusto carry out the steps of: maintaining, by each messaging unit, a statusof unused message buffers in the messaging unit's memory; andrequesting, by a process on a compute node via a broadcast operation,availability of messaging unit message buffers; receiving, from eachmessaging unit, an indication of available messaging unit messagebuffers; and sending, by the process, a data communications message toan available messaging unit message buffer with an instruction toinitialize an on-demand process associated with that messaging unitmessage buffer.
 10. The apparatus of claim 8 further comprising computerprogram instructions that, when executed, cause the apparatus to carryout the steps of: receiving, by a messaging unit, a request toinitialize an on-demand process to execute a task, including receiving adata communications message intended for the on-demand process; storing,by the messaging unit in the messaging unit's computer memory, the datacommunications message intended for the on-demand process in anavailable message buffer; initializing the on-demand process;establishing, by the on-demand process upon initialization, a messagingbuffer in main memory of the compute node; copying, by the on-demandprocess, the data communications message from the message buffer of themessaging unit into the message buffer of main memory; and uponcompletion of the task, releasing, by the on-demand process, the messagebuffer in the main memory and the message buffer in the messaging unit.11. The apparatus of claim 8 wherein: the parallel computer comprises aparallel active messaging interface (‘PAMI’) and the plurality ofcompute nodes execute a parallel application, the PAMI comprises datacommunications endpoints, each endpoint comprising a specification ofdata communications parameters for a thread of execution on a computenode, including specifications of a client, a context, and a task, theendpoints coupled for data communications through the PAMI; and eachprocess comprises an endpoint.
 12. The apparatus of claim 11 wherein:each client comprises a collection of data communications resourcesdedicated to the exclusive use of an application-level data processingentity; each context comprises a subset of the collection of dataprocessing resources of a client, context functions, and a work queue ofdata transfer instructions to be performed by use of the subset throughthe context functions operated by an assigned thread of execution; andeach task represents a process of execution of the parallel application.13. The apparatus of claim 11 wherein each context carries out, throughpost and advance functions, data communications for the parallelapplication on data communications resources in the exclusive possessionof that context.
 14. The apparatus of claim 11 wherein each contextcarries out data communications operations independently and in parallelwith other contexts.
 15. A computer program product for internode datacommunications in a parallel computer, the parallel computer comprisinga plurality of compute nodes, each compute node comprising main computermemory and a messaging unit, the messaging unit comprising computermemory, the messaging unit comprising a module of automated computingmachinery coupling compute nodes for data communications, the computerprogram product disposed upon a computer readable storage medium, thecomputer program product comprising computer program instructions that,when executed, cause a computer to carry out the steps of: for eachcompute node at compute node boot time: allocating, by the messagingunit in the messaging unit's computer memory, a predefined number ofmessage buffers, each message buffer associated with a process to beinitialized on the compute node; receiving, by the messaging unit priorto initialization of a particular process on the compute node, a datacommunications message intended for the particular process; storing thedata communications message in the message buffer associated with theparticular process; upon initialization of the particular process,establishing, by the particular process, a messaging buffer in mainmemory of the compute node; and copying, by the particular process, thedata communications message from the message buffer of the messagingunit into the message buffer of main memory.
 16. The computer programproduct of claim 15 further comprising computer program instructionsthat, when executed, cause the computer to carry out the steps of:maintaining, by each messaging unit, a status of unused message buffersin the messaging unit's memory; and requesting, by a process on acompute node via a broadcast operation, availability of messaging unitmessage buffers; receiving, from each messaging unit, an indication ofavailable messaging unit message buffers; and sending, by the process, adata communications message to an available messaging unit messagebuffer with an instruction to initialize an on-demand process associatedwith that messaging unit message buffer.
 17. The computer programproduct of claim 15 further comprising computer program instructionsthat, when executed, cause the computer to carry out the steps of:receiving, by a messaging unit, a request to initialize an on-demandprocess to execute a task, including receiving a data communicationsmessage intended for the on-demand process; storing, by the messagingunit in the messaging unit's computer memory, the data communicationsmessage intended for the on-demand process in an available messagebuffer; initializing the on-demand process; establishing, by theon-demand process upon initialization, a messaging buffer in main memoryof the compute node; copying, by the on-demand process, the datacommunications message from the message buffer of the messaging unitinto the message buffer of main memory; and upon completion of the task,releasing, by the on-demand process, the message buffer in the mainmemory and the message buffer in the messaging unit.
 18. The computerprogram product of claim 15 wherein: the parallel computer comprises aparallel active messaging interface (‘PAMI’) and the plurality ofcompute nodes execute a parallel application, the PAMI comprises datacommunications endpoints, each endpoint comprising a specification ofdata communications parameters for a thread of execution on a computenode, including specifications of a client, a context, and a task, theendpoints coupled for data communications through the PAMI; and eachprocess comprises an endpoint.
 19. The computer program product of claim18 wherein: each client comprises a collection of data communicationsresources dedicated to the exclusive use of an application-level dataprocessing entity; each context comprises a subset of the collection ofdata processing resources of a client, context functions, and a workqueue of data transfer instructions to be performed by use of the subsetthrough the context functions operated by an assigned thread ofexecution; and each task represents a process of execution of theparallel application.
 20. The computer program product of claim 18wherein each context carries out, through post and advance functions,data communications for the parallel application on data communicationsresources in the exclusive possession of that context.
 21. The computerprogram product of claim 18 wherein each context carries out datacommunications operations independently and in parallel with othercontexts. 22-23. (canceled)